Skip to main content

Research Repository

See what's under the surface

Advanced Search

A study on probability of distribution loads based on expectation maximization algorithm

Ganjavi, Amin; Christopher, Edward; Johnson, Christopher Mark; Clare, Jon C.

Authors

Amin Ganjavi

Edward Christopher edward.christopher@nottingham.ac.uk

Christopher Mark Johnson mark.johnson@nottingham.ac.uk

Jon C. Clare jon.clare@nottingham.ac.uk



Abstract

In a distribution power network, the load model has no certain pattern or predicted behaviour due to large range of data and changes in energy consumption for end-user consumers. Thus, a powerful analysis based on probabilistic structure is required. For this paper Gaussian Mixture Model (GMM) has been used. GMM is a powerful probability model that allows different types of load distributions to be presented as a combination of several Gaussian distributions. The parameters of GMM is unknown for large random data such as real load data and these parameters can be identified by Expectation Maximization (EM) algorithm. This paper presents a method to evaluate probabilistic load data concerning the time-evolution of any type of distribution load for any duration of time. The proposed method is explained through generated load data of 100 residential houses for duration of one year.

Publication Date Oct 30, 2017
Electronic ISSN 2472-8152
Peer Reviewed Peer Reviewed
APA6 Citation Ganjavi, A., Christopher, E., Johnson, C. M., & Clare, J. C. (2017). A study on probability of distribution loads based on expectation maximization algorithm. doi:10.1109/ISGT.2017.8086037
DOI https://doi.org/10.1109/ISGT.2017.8086037
Keywords Expectation Maximization, Gaussian Mixture Model, Load forecasting and Probability, Probability Density Function
Publisher URL http://ieeexplore.ieee.org/abstract/document/8086037/
Related Public URLs http://www.ieee-pes.org...-technologies-isgt-2017
http://sites.ieee.org/isgt-2017/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
;